Paper: Without a ’doubt’? Unsupervised Discovery of Downward-Entailing Operators

ACL ID N09-1016
Title Without a ’doubt’? Unsupervised Discovery of Downward-Entailing Operators
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

An important part of textual inference is mak- ing deductions involving monotonicity, that is, determining whether a given assertion en- tails restrictions or relaxations of that asser- tion. For instance, the statement ‘We know the epidemic spread quickly’ does not entail ‘We know the epidemic spread quickly via fleas’, but ‘We doubt the epidemic spread quickly’ entails ‘We doubt the epidemic spread quickly via fleas’. Here, we present the first algorithm for the challenging lexical-semantics prob- lem of learning linguistic constructions that, like ‘doubt’, are downward entailing (DE). Our algorithm is unsupervised, resource-lean, and effective, accurately recovering many DE operators that are missing from the hand- constructed lists that textual-inference sys- tems c...